PxEBCA: Proximity Expansion Based Clustering Algorithm

Cluster analysis is one of the main techniques for analysing data. It is a technique for detecting groups of objects which are similar without specifying any criteria for the grouping. The matter of detecting clusters is challenging when the clusters are of varied size, density and shape. DBSCAN can...

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Published inTurkish journal of computer and mathematics education Vol. 12; no. 5; pp. 852 - 856
Main Authors Arora, Bhumika S, Chavda, Vijay, Pandya, Bhadresh R
Format Journal Article
LanguageEnglish
Published Gurgaon Ninety Nine Publication 11.04.2021
Subjects
Algorithms
Cluster analysis
Clustering
Computers
Data Analysis
Database Management Systems
Datasets
Density
Experiments
Multivariate Analysis
Noise
Object recognition
Outliers (statistics)
Online AccessGet full text
ISSN1309-4653
1309-4653
DOI10.17762/turcomat.v12i5.1494

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Abstract Cluster analysis is one of the main techniques for analysing data. It is a technique for detecting groups of objects which are similar without specifying any criteria for the grouping. The matter of detecting clusters is challenging when the clusters are of varied size, density and shape. DBSCAN can find arbitrary shaped clusters along with outliers but it cannot handle different density. This paper presents a new method for detecting density based clusters which works on datasets having varied density. In this paper we propose PxEBCA that discovers clusters with arbitrary shape and also with varying density.Experimental evaluation of the effectiveness and efficiency of PEBCA was done using synthetic data. The results of experiments demonstrated that PxEBCA is significantly more effective in discovering clusters of arbitrary shapes with varying densities.
AbstractList Cluster analysis is one of the main techniques for analysing data. It is a technique for detecting groups of objects which are similar without specifying any criteria for the grouping. The matter of detecting clusters is challenging when the clusters are of varied size, density and shape. DBSCAN can find arbitrary shaped clusters along with outliers but it cannot handle different density. This paper presents a new method for detecting density based clusters which works on datasets having varied density. In this paper we propose PxEBCA that discovers clusters with arbitrary shape and also with varying density.Experimental evaluation of the effectiveness and efficiency of PEBCA was done using synthetic data. The results of experiments demonstrated that PxEBCA is significantly more effective in discovering clusters of arbitrary shapes with varying densities._
Author Dr Bhadresh R. Pandya, Bhumika S. Arora, Dr.Vijay Chavda
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StartPage 852
SubjectTerms Algorithms
Cluster analysis
Clustering
Computers
Data Analysis
Database Management Systems
Datasets
Density
Experiments
Multivariate Analysis
Noise
Object recognition
Outliers (statistics)
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